Support vector data description (SVDD) is a machine-learning technique used for single class classification and outlier or anomaly detection. The SVDD classifier partitions the whole space into an inlier region which consists of the region near the training data, and an outlier region which consists of points away from the training data. The computation of the SVDD classifier uses a kernel function with the Gaussian kernel being a common choice for the kernel function. The Gaussian kernel has a bandwidth parameter, and it is important to set the value of this parameter correctly for good results. A small bandwidth leads to over-fitting and the resulting SVDD classifier overestimates the number of anomalies, while a large bandwidth leads to under-fitting and the resulting SVDD classifier underestimates the number of anomalies resulting in possibly many anomalies or outliers not being detected by the classifier.